Overview

Dataset statistics

Number of variables20
Number of observations5271
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory715.6 KiB
Average record size in memory139.0 B

Variable types

Numeric11
Categorical9

Alerts

Ascites is highly overall correlated with Edema_YHigh correlation
Bilirubin is highly overall correlated with CopperHigh correlation
Copper is highly overall correlated with BilirubinHigh correlation
Edema_N is highly overall correlated with Edema_S and 1 other fieldsHigh correlation
Edema_S is highly overall correlated with Edema_NHigh correlation
Edema_Y is highly overall correlated with Ascites and 1 other fieldsHigh correlation
Hepatomegaly is highly overall correlated with StageHigh correlation
Stage is highly overall correlated with HepatomegalyHigh correlation
is_male is highly imbalanced (61.7%)Imbalance
Ascites is highly imbalanced (73.0%)Imbalance
Edema_N is highly imbalanced (55.7%)Imbalance
Edema_S is highly imbalanced (71.5%)Imbalance
Edema_Y is highly imbalanced (74.7%)Imbalance

Reproduction

Analysis started2024-01-03 07:56:24.553557
Analysis finished2024-01-03 07:56:35.653894
Duration11.1 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

N_years
Real number (ℝ)

Distinct409
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5854703
Minimum0.11232877
Maximum13.136986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.3 KiB
2024-01-03T11:26:35.724217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.11232877
5-th percentile0.91506849
Q13.3808219
median5.1561644
Q37.3753425
95-th percentile11.463014
Maximum13.136986
Range13.024658
Interquartile range (IQR)3.9945205

Descriptive statistics

Standard deviation2.9776534
Coefficient of variation (CV)0.53310702
Kurtosis-0.46751299
Mean5.5854703
Median Absolute Deviation (MAD)1.9013699
Skewness0.42978853
Sum29441.014
Variance8.8664198
MonotonicityNot monotonic
2024-01-03T11:26:35.822249image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.331506849 82
 
1.6%
3.928767123 61
 
1.2%
2.106849315 52
 
1.0%
5.156164384 46
 
0.9%
9.438356164 42
 
0.8%
4.890410959 41
 
0.8%
0.9150684932 39
 
0.7%
6.284931507 39
 
0.7%
6.093150685 37
 
0.7%
5.967123288 36
 
0.7%
Other values (399) 4796
91.0%
ValueCountFrequency (%)
0.1123287671 17
0.3%
0.1178082192 2
 
< 0.1%
0.1397260274 14
0.3%
0.1863013699 1
 
< 0.1%
0.1945205479 8
0.2%
0.2109589041 10
0.2%
0.301369863 13
0.2%
0.3561643836 4
 
0.1%
0.3589041096 11
0.2%
0.3753424658 1
 
< 0.1%
ValueCountFrequency (%)
13.1369863 7
 
0.1%
12.48219178 26
0.5%
12.39178082 9
 
0.2%
12.35342466 15
0.3%
12.32876712 26
0.5%
12.23835616 12
0.2%
12.21643836 24
0.5%
12.2 20
0.4%
12.12876712 8
 
0.2%
11.95890411 28
0.5%

Age
Real number (ℝ)

Distinct363
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.678786
Minimum26.29589
Maximum78.493151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.3 KiB
2024-01-03T11:26:35.917001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum26.29589
5-th percentile33.717808
Q143.09589
median51.523288
Q356.668493
95-th percentile67.046575
Maximum78.493151
Range52.19726
Interquartile range (IQR)13.572603

Descriptive statistics

Standard deviation9.8189008
Coefficient of variation (CV)0.19374775
Kurtosis-0.41688082
Mean50.678786
Median Absolute Deviation (MAD)6.8657534
Skewness-0.019642748
Sum267127.88
Variance96.410813
MonotonicityNot monotonic
2024-01-03T11:26:36.009805image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52.21917808 47
 
0.9%
56.43013699 47
 
0.9%
61.28493151 42
 
0.8%
61.3369863 40
 
0.8%
55.49041096 39
 
0.7%
40.74520548 38
 
0.7%
52.03561644 38
 
0.7%
61 37
 
0.7%
44.6 37
 
0.7%
62.90410959 37
 
0.7%
Other values (353) 4869
92.4%
ValueCountFrequency (%)
26.29589041 10
0.2%
28.90410959 13
0.2%
29.57534247 4
 
0.1%
29.69589041 1
 
< 0.1%
30.29589041 7
0.1%
30.59452055 15
0.3%
30.88493151 16
0.3%
31.22739726 1
 
< 0.1%
31.40273973 16
0.3%
31.46575342 11
0.2%
ValueCountFrequency (%)
78.49315068 19
0.4%
76.76164384 5
 
0.1%
75.0630137 19
0.4%
75.05205479 1
 
< 0.1%
74.57534247 14
0.3%
72.82191781 6
 
0.1%
71.94246575 4
 
0.1%
71.40821918 1
 
< 0.1%
70.95616438 14
0.3%
70.88493151 4
 
0.1%

is_male
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size309.0 KiB
0.0
4877 
1.0
 
394

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15813
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4877
92.5%
1.0 394
 
7.5%

Length

2024-01-03T11:26:36.089285image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T11:26:36.156187image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4877
92.5%
1.0 394
 
7.5%

Most occurring characters

ValueCountFrequency (%)
0 10148
64.2%
. 5271
33.3%
1 394
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10542
66.7%
Other Punctuation 5271
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10148
96.3%
1 394
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 5271
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15813
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10148
64.2%
. 5271
33.3%
1 394
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15813
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10148
64.2%
. 5271
33.3%
1 394
 
2.5%

Ascites
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size298.7 KiB
0
5027 
1
 
244

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5271
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5027
95.4%
1 244
 
4.6%

Length

2024-01-03T11:26:36.220673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T11:26:36.280063image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5027
95.4%
1 244
 
4.6%

Most occurring characters

ValueCountFrequency (%)
0 5027
95.4%
1 244
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5271
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5027
95.4%
1 244
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
Common 5271
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5027
95.4%
1 244
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5271
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5027
95.4%
1 244
 
4.6%

Hepatomegaly
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size298.7 KiB
1
2730 
0
2541 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5271
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 2730
51.8%
0 2541
48.2%

Length

2024-01-03T11:26:36.348196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T11:26:36.414590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2730
51.8%
0 2541
48.2%

Most occurring characters

ValueCountFrequency (%)
1 2730
51.8%
0 2541
48.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5271
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2730
51.8%
0 2541
48.2%

Most occurring scripts

ValueCountFrequency (%)
Common 5271
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2730
51.8%
0 2541
48.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5271
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2730
51.8%
0 2541
48.2%

Spiders
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size298.7 KiB
0
3972 
1
1299 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5271
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3972
75.4%
1 1299
 
24.6%

Length

2024-01-03T11:26:36.493881image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T11:26:36.566177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3972
75.4%
1 1299
 
24.6%

Most occurring characters

ValueCountFrequency (%)
0 3972
75.4%
1 1299
 
24.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5271
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3972
75.4%
1 1299
 
24.6%

Most occurring scripts

ValueCountFrequency (%)
Common 5271
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3972
75.4%
1 1299
 
24.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5271
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3972
75.4%
1 1299
 
24.6%

Bilirubin
Real number (ℝ)

HIGH CORRELATION 

Distinct108
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6003889
Minimum0.3
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.3 KiB
2024-01-03T11:26:36.828620image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.5
Q10.7
median1.1
Q33
95-th percentile9.95
Maximum28
Range27.7
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation3.8523952
Coefficient of variation (CV)1.4814689
Kurtosis13.729631
Mean2.6003889
Median Absolute Deviation (MAD)0.5
Skewness3.424573
Sum13706.65
Variance14.840949
MonotonicityNot monotonic
2024-01-03T11:26:36.928053image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 609
 
11.6%
0.9 422
 
8.0%
0.8 405
 
7.7%
0.7 398
 
7.6%
0.5 388
 
7.4%
1.1 294
 
5.6%
1.3 231
 
4.4%
1 174
 
3.3%
0.4 123
 
2.3%
3.2 111
 
2.1%
Other values (98) 2116
40.1%
ValueCountFrequency (%)
0.3 30
 
0.6%
0.4 123
 
2.3%
0.5 388
7.4%
0.6 609
11.6%
0.7 398
7.6%
0.8 405
7.7%
0.9 422
8.0%
1 174
 
3.3%
1.1 294
5.6%
1.2 99
 
1.9%
ValueCountFrequency (%)
28 14
0.3%
25.5 10
 
0.2%
24.5 9
 
0.2%
22.5 6
 
0.1%
21.6 12
 
0.2%
20 9
 
0.2%
18 1
 
< 0.1%
17.9 17
0.3%
17.4 33
0.6%
17.2 4
 
0.1%

Cholesterol
Real number (ℝ)

Distinct222
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean352.48644
Minimum120
Maximum1775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.3 KiB
2024-01-03T11:26:37.024630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile201
Q1248
median299
Q3390
95-th percentile652
Maximum1775
Range1655
Interquartile range (IQR)142

Descriptive statistics

Standard deviation200.43899
Coefficient of variation (CV)0.56864313
Kurtosis17.695544
Mean352.48644
Median Absolute Deviation (MAD)63
Skewness3.6686841
Sum1857956
Variance40175.788
MonotonicityNot monotonic
2024-01-03T11:26:37.122491image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
248 91
 
1.7%
232 88
 
1.7%
316 84
 
1.6%
263 83
 
1.6%
448 80
 
1.5%
374 79
 
1.5%
260 79
 
1.5%
298 75
 
1.4%
280 74
 
1.4%
273 69
 
1.3%
Other values (212) 4469
84.8%
ValueCountFrequency (%)
120 6
 
0.1%
127 14
 
0.3%
132 21
0.4%
149 3
 
0.1%
151 10
 
0.2%
168 8
 
0.2%
172 6
 
0.1%
174 10
 
0.2%
175 39
0.7%
176 8
 
0.2%
ValueCountFrequency (%)
1775 12
0.2%
1712 11
0.2%
1600 12
0.2%
1480 9
0.2%
1336 12
0.2%
1280 1
 
< 0.1%
1276 12
0.2%
1236 1
 
< 0.1%
1232 1
 
< 0.1%
1128 13
0.2%

Albumin
Real number (ℝ)

Distinct154
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5380706
Minimum1.96
Maximum4.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.3 KiB
2024-01-03T11:26:37.215748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.96
5-th percentile2.94
Q13.35
median3.57
Q33.77
95-th percentile4.09
Maximum4.64
Range2.68
Interquartile range (IQR)0.42

Descriptive statistics

Standard deviation0.35488579
Coefficient of variation (CV)0.10030489
Kurtosis1.1571997
Mean3.5380706
Median Absolute Deviation (MAD)0.22
Skewness-0.57891344
Sum18649.17
Variance0.12594392
MonotonicityNot monotonic
2024-01-03T11:26:37.310544image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.35 245
 
4.6%
3.6 234
 
4.4%
3.7 211
 
4.0%
3.85 150
 
2.8%
3.77 144
 
2.7%
3.26 144
 
2.7%
3.5 132
 
2.5%
3.65 129
 
2.4%
3.2 115
 
2.2%
3.57 112
 
2.1%
Other values (144) 3655
69.3%
ValueCountFrequency (%)
1.96 3
 
0.1%
1.97 1
 
< 0.1%
2.1 5
 
0.1%
2.23 3
 
0.1%
2.27 2
 
< 0.1%
2.31 5
 
0.1%
2.33 9
 
0.2%
2.35 1
 
< 0.1%
2.43 29
0.6%
2.5 1
 
< 0.1%
ValueCountFrequency (%)
4.64 6
 
0.1%
4.52 5
 
0.1%
4.43 1
 
< 0.1%
4.4 8
 
0.2%
4.38 10
 
0.2%
4.3 37
0.7%
4.27 1
 
< 0.1%
4.24 9
 
0.2%
4.23 14
 
0.3%
4.22 10
 
0.2%

Copper
Real number (ℝ)

HIGH CORRELATION 

Distinct164
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.701679
Minimum4
Maximum588
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.3 KiB
2024-01-03T11:26:37.399140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile15
Q139
median65
Q3102
95-th percentile231
Maximum588
Range584
Interquartile range (IQR)63

Descriptive statistics

Standard deviation77.542064
Coefficient of variation (CV)0.91547258
Kurtosis11.653368
Mean84.701679
Median Absolute Deviation (MAD)29
Skewness2.8617216
Sum446462.55
Variance6012.7717
MonotonicityNot monotonic
2024-01-03T11:26:37.494397image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 228
 
4.3%
67 227
 
4.3%
75 142
 
2.7%
39 134
 
2.5%
20 130
 
2.5%
58 126
 
2.4%
38 111
 
2.1%
13 111
 
2.1%
41 105
 
2.0%
44 104
 
2.0%
Other values (154) 3853
73.1%
ValueCountFrequency (%)
4 6
 
0.1%
9 27
 
0.5%
10 14
 
0.3%
11 44
 
0.8%
12 21
 
0.4%
12.7 1
 
< 0.1%
13 111
2.1%
14 32
 
0.6%
15 9
 
0.2%
16 7
 
0.1%
ValueCountFrequency (%)
588 19
0.4%
558 9
0.2%
464 20
0.4%
444 12
0.2%
412 3
 
0.1%
380 21
0.4%
358 12
0.2%
308 3
 
0.1%
290 15
0.3%
281 5
 
0.1%

Alk_Phos
Real number (ℝ)

Distinct362
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1811.2333
Minimum289
Maximum13862.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.3 KiB
2024-01-03T11:26:37.590768image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum289
5-th percentile614
Q1823
median1142
Q31838.5
95-th percentile6064.8
Maximum13862.4
Range13573.4
Interquartile range (IQR)1015.5

Descriptive statistics

Standard deviation1935.3515
Coefficient of variation (CV)1.0685269
Kurtosis11.703609
Mean1811.2333
Median Absolute Deviation (MAD)451
Skewness3.2187069
Sum9547010.8
Variance3745585.4
MonotonicityNot monotonic
2024-01-03T11:26:37.694711image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
663 86
 
1.6%
794 77
 
1.5%
645 62
 
1.2%
1636 61
 
1.2%
944 53
 
1.0%
674 44
 
0.8%
7277 43
 
0.8%
1052 42
 
0.8%
1345 40
 
0.8%
1440 38
 
0.7%
Other values (352) 4725
89.6%
ValueCountFrequency (%)
289 13
0.2%
310 17
0.3%
369 9
0.2%
377 5
 
0.1%
414 9
0.2%
423 21
0.4%
453 12
0.2%
466 2
 
< 0.1%
516 6
 
0.1%
554 21
0.4%
ValueCountFrequency (%)
13862.4 12
0.2%
12285 1
 
< 0.1%
12258.8 17
0.3%
11552 8
0.2%
11320.2 13
0.2%
11046.6 7
 
0.1%
10396.8 18
0.3%
10165 11
0.2%
9933.2 3
 
0.1%
9066.8 14
0.3%

SGOT
Real number (ℝ)

Distinct195
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.58753
Minimum26.35
Maximum457.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.3 KiB
2024-01-03T11:26:37.793019image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum26.35
5-th percentile54.25
Q175
median106.95
Q3137.95
95-th percentile198.4
Maximum457.25
Range430.9
Interquartile range (IQR)62.95

Descriptive statistics

Standard deviation48.964789
Coefficient of variation (CV)0.4310754
Kurtosis6.8408319
Mean113.58753
Median Absolute Deviation (MAD)31
Skewness1.6710329
Sum598719.85
Variance2397.5505
MonotonicityNot monotonic
2024-01-03T11:26:37.883536image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.3 190
 
3.6%
57.35 153
 
2.9%
137.95 142
 
2.7%
128.65 132
 
2.5%
170.5 127
 
2.4%
97.65 119
 
2.3%
120.9 116
 
2.2%
93 113
 
2.1%
106.95 97
 
1.8%
122.45 87
 
1.7%
Other values (185) 3995
75.8%
ValueCountFrequency (%)
26.35 5
 
0.1%
28.38 13
 
0.2%
41.85 16
 
0.3%
43.4 24
0.5%
45 12
 
0.2%
46.5 6
 
0.1%
49 1
 
< 0.1%
49.6 38
0.7%
50 1
 
< 0.1%
51.15 50
0.9%
ValueCountFrequency (%)
457.25 14
0.3%
338 5
 
0.1%
328.6 7
0.1%
299.15 4
 
0.1%
288 8
0.2%
283.05 1
 
< 0.1%
280.55 12
0.2%
272.8 7
0.1%
267 1
 
< 0.1%
261.86 1
 
< 0.1%

Tryglicerides
Real number (ℝ)

Distinct155
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean115.28609
Minimum33
Maximum598
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.3 KiB
2024-01-03T11:26:37.978063image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile56
Q184
median104
Q3138
95-th percentile210
Maximum598
Range565
Interquartile range (IQR)54

Descriptive statistics

Standard deviation52.60278
Coefficient of variation (CV)0.45628036
Kurtosis12.672583
Mean115.28609
Median Absolute Deviation (MAD)26
Skewness2.4450711
Sum607673
Variance2767.0525
MonotonicityNot monotonic
2024-01-03T11:26:38.080065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 168
 
3.2%
85 155
 
2.9%
118 141
 
2.7%
68 137
 
2.6%
91 134
 
2.5%
56 126
 
2.4%
101 122
 
2.3%
55 118
 
2.2%
108 112
 
2.1%
104 108
 
2.0%
Other values (145) 3950
74.9%
ValueCountFrequency (%)
33 14
 
0.3%
44 26
 
0.5%
46 8
 
0.2%
49 10
 
0.2%
50 23
 
0.4%
52 25
 
0.5%
53 14
 
0.3%
55 118
2.2%
56 126
2.4%
57 2
 
< 0.1%
ValueCountFrequency (%)
598 6
 
0.1%
432 15
0.3%
394 1
 
< 0.1%
382 6
 
0.1%
328 1
 
< 0.1%
322 3
 
0.1%
319 3
 
0.1%
318 12
0.2%
309 9
0.2%
280 13
0.2%

Platelets
Real number (ℝ)

Distinct223
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean264.02371
Minimum62
Maximum563
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.3 KiB
2024-01-03T11:26:38.172562image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum62
5-th percentile128
Q1209
median259
Q3317
95-th percentile430
Maximum563
Range501
Interquartile range (IQR)108

Descriptive statistics

Standard deviation87.584068
Coefficient of variation (CV)0.33172803
Kurtosis0.34752348
Mean264.02371
Median Absolute Deviation (MAD)53
Skewness0.42541087
Sum1391669
Variance7670.9689
MonotonicityNot monotonic
2024-01-03T11:26:38.265762image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
344 160
 
3.0%
336 111
 
2.1%
181 108
 
2.0%
213 106
 
2.0%
265 92
 
1.7%
268 90
 
1.7%
295 89
 
1.7%
165 87
 
1.7%
251 86
 
1.6%
231 85
 
1.6%
Other values (213) 4257
80.8%
ValueCountFrequency (%)
62 12
0.2%
70 12
0.2%
71 11
0.2%
75 1
 
< 0.1%
76 2
 
< 0.1%
79 7
0.1%
80 16
0.3%
81 9
0.2%
88 1
 
< 0.1%
92 1
 
< 0.1%
ValueCountFrequency (%)
563 26
0.5%
539 4
 
0.1%
518 9
 
0.2%
517 3
 
0.1%
516 1
 
< 0.1%
514 9
 
0.2%
493 6
 
0.1%
487 8
 
0.2%
474 7
 
0.1%
471 16
0.3%

Prothrombin
Real number (ℝ)

Distinct47
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.632865
Minimum9
Maximum15.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.3 KiB
2024-01-03T11:26:38.350242image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile9.6
Q110
median10.6
Q311
95-th percentile12.05
Maximum15.2
Range6.2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.79271131
Coefficient of variation (CV)0.074552939
Kurtosis2.3426417
Mean10.632865
Median Absolute Deviation (MAD)0.5
Skewness1.1209872
Sum56045.83
Variance0.62839122
MonotonicityNot monotonic
2024-01-03T11:26:38.437703image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
10.6 739
 
14.0%
11 549
 
10.4%
10 408
 
7.7%
9.9 344
 
6.5%
9.8 290
 
5.5%
10.1 234
 
4.4%
10.9 218
 
4.1%
10.2 192
 
3.6%
11.5 188
 
3.6%
10.3 185
 
3.5%
Other values (37) 1924
36.5%
ValueCountFrequency (%)
9 11
 
0.2%
9.1 6
 
0.1%
9.2 9
 
0.2%
9.3 2
 
< 0.1%
9.4 14
 
0.3%
9.5 112
 
2.1%
9.6 180
3.4%
9.7 144
2.7%
9.8 290
5.5%
9.9 344
6.5%
ValueCountFrequency (%)
15.2 8
 
0.2%
14.1 3
 
0.1%
13.6 9
 
0.2%
13.3 2
 
< 0.1%
13.2 28
0.5%
13.1 1
 
< 0.1%
13 43
0.8%
12.9 22
0.4%
12.8 1
 
< 0.1%
12.7 17
 
0.3%

Stage
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size309.0 KiB
3.0
2122 
4.0
1792 
2.0
1117 
1.0
240 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15813
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row4.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.0 2122
40.3%
4.0 1792
34.0%
2.0 1117
21.2%
1.0 240
 
4.6%

Length

2024-01-03T11:26:38.521712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T11:26:38.587763image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 2122
40.3%
4.0 1792
34.0%
2.0 1117
21.2%
1.0 240
 
4.6%

Most occurring characters

ValueCountFrequency (%)
. 5271
33.3%
0 5271
33.3%
3 2122
13.4%
4 1792
 
11.3%
2 1117
 
7.1%
1 240
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10542
66.7%
Other Punctuation 5271
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5271
50.0%
3 2122
20.1%
4 1792
 
17.0%
2 1117
 
10.6%
1 240
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 5271
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 15813
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 5271
33.3%
0 5271
33.3%
3 2122
13.4%
4 1792
 
11.3%
2 1117
 
7.1%
1 240
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15813
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 5271
33.3%
0 5271
33.3%
3 2122
13.4%
4 1792
 
11.3%
2 1117
 
7.1%
1 240
 
1.5%

took_drug
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size298.7 KiB
0
2694 
1
2577 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5271
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 2694
51.1%
1 2577
48.9%

Length

2024-01-03T11:26:38.661063image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T11:26:38.721413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2694
51.1%
1 2577
48.9%

Most occurring characters

ValueCountFrequency (%)
0 2694
51.1%
1 2577
48.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5271
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2694
51.1%
1 2577
48.9%

Most occurring scripts

ValueCountFrequency (%)
Common 5271
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2694
51.1%
1 2577
48.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5271
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2694
51.1%
1 2577
48.9%

Edema_N
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size298.7 KiB
1
4786 
0
485 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5271
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 4786
90.8%
0 485
 
9.2%

Length

2024-01-03T11:26:38.787716image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T11:26:38.847614image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4786
90.8%
0 485
 
9.2%

Most occurring characters

ValueCountFrequency (%)
1 4786
90.8%
0 485
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5271
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4786
90.8%
0 485
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 5271
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4786
90.8%
0 485
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5271
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4786
90.8%
0 485
 
9.2%

Edema_S
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size298.7 KiB
0
5009 
1
 
262

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5271
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5009
95.0%
1 262
 
5.0%

Length

2024-01-03T11:26:38.913737image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T11:26:38.975333image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5009
95.0%
1 262
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 5009
95.0%
1 262
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5271
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5009
95.0%
1 262
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5271
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5009
95.0%
1 262
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5271
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5009
95.0%
1 262
 
5.0%

Edema_Y
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size298.7 KiB
0
5048 
1
 
223

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5271
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5048
95.8%
1 223
 
4.2%

Length

2024-01-03T11:26:39.040207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-03T11:26:39.109993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5048
95.8%
1 223
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 5048
95.8%
1 223
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5271
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5048
95.8%
1 223
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
Common 5271
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5048
95.8%
1 223
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5271
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5048
95.8%
1 223
 
4.2%

Interactions

2024-01-03T11:26:34.329868image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:25.397177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:26.704330image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:27.581310image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:28.351649image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:29.266975image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:30.040177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:30.825452image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:31.626913image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:32.515355image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:33.411601image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:34.402894image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:25.535862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:26.789122image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:27.655088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:28.427144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:29.340194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:30.112725image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:30.900817image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:31.696431image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:32.589307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:33.492659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:34.473000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:25.673125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:26.857657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:27.723941image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:28.501253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:29.409029image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:30.181510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:30.971915image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:31.764039image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:32.660825image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:33.575699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:34.543136image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:25.825255image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:26.927935image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:27.793484image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:28.572971image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:29.475862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:30.251130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:31.043518image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:31.827389image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:32.738343image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:33.711276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:34.617242image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:26.013385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:26.992547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:27.858283image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:28.647847image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:29.549669image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:30.323319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:31.119666image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:31.896165image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:32.821322image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:33.807045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:34.687477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:26.086055image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:27.055918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:27.926366image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:28.721027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:29.616706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:30.392256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:31.191932image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:31.969098image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:32.898316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:33.889284image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:34.760762image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:26.160401image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:27.119547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:27.990263image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:28.797558image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:29.688248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:30.462985image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:31.265955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:32.039939image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:32.978471image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:33.973218image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:34.836074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:26.237817image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:27.191322image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:28.065417image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:28.991127image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:29.762036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:30.542751image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:31.343009image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:32.249615image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:33.079697image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:34.057466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:34.902303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:26.304659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:27.260447image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:28.129409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:29.055639image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:29.825498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:30.608659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:31.408061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:32.310639image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:33.161213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:34.122163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:34.972513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:26.391068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:27.364933image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:28.208822image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:29.125265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:29.897597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:30.681506image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:31.481103image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:32.380812image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:33.244706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:34.191426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:35.142679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:26.565754image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:27.490203image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:28.277558image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:29.193413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:29.966723image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:30.750550image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:31.549640image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:32.445057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:33.323224image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-01-03T11:26:34.256950image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-01-03T11:26:39.169148image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
AgeAlbuminAlk_PhosAscitesBilirubinCholesterolCopperEdema_NEdema_SEdema_YHepatomegalyN_yearsPlateletsProthrombinSGOTSpidersStageTrygliceridesis_maletook_drug
Age1.000-0.075-0.0570.1750.075-0.0860.0470.2020.1330.2010.122-0.128-0.1230.142-0.0320.0950.0930.0030.1620.120
Albumin-0.0751.000-0.1850.388-0.294-0.061-0.2700.3210.0550.4110.2710.2480.126-0.179-0.2240.2320.154-0.1080.0610.112
Alk_Phos-0.057-0.1851.0000.1480.3320.3140.2890.1070.0960.1370.198-0.1400.0470.0860.4250.0880.0620.2060.0530.050
Ascites0.1750.3880.1481.0000.257-0.1090.2120.4940.0620.6380.186-0.230-0.1760.2650.1070.1950.1920.1080.0000.000
Bilirubin0.075-0.2940.3320.2571.0000.3380.5880.3330.1490.3740.338-0.388-0.1680.2740.4980.3060.1410.2970.1120.082
Cholesterol-0.086-0.0610.314-0.1090.3381.0000.2630.0610.0180.0650.130-0.1150.127-0.0490.3550.0960.0190.3420.0500.078
Copper0.047-0.2700.2890.2120.5880.2631.0000.2870.1250.3100.321-0.339-0.1170.2260.4390.2790.1420.3220.1630.075
Edema_N0.2020.3210.1070.4940.3330.0610.2871.0000.7170.6590.2080.2260.173-0.288-0.1030.2450.210-0.0870.0000.000
Edema_S0.1330.0550.0960.0620.1490.0180.1250.7171.0000.0440.116-0.093-0.0650.1460.0410.1330.1000.0370.0180.000
Edema_Y0.2010.4110.1370.6380.3740.0650.3100.6590.0441.0000.171-0.224-0.1790.2560.1030.2060.1910.0850.0000.000
Hepatomegaly0.1220.2710.1980.1860.3380.1300.3210.2080.1160.1711.000-0.246-0.1850.2630.2170.3200.5080.1720.0390.059
N_years-0.1280.248-0.140-0.230-0.388-0.115-0.3390.226-0.093-0.224-0.2461.0000.128-0.138-0.2440.2670.168-0.1810.0580.068
Platelets-0.1230.1260.047-0.176-0.1680.127-0.1170.173-0.065-0.179-0.1850.1281.000-0.180-0.0170.2140.1390.0150.0310.073
Prothrombin0.142-0.1790.0860.2650.274-0.0490.226-0.2880.1460.2560.263-0.138-0.1801.0000.1360.3140.2030.0070.0590.090
SGOT-0.032-0.2240.4250.1070.4980.3550.439-0.1030.0410.1030.217-0.244-0.0170.1361.0000.1750.0780.1640.0640.090
Spiders0.0950.2320.0880.1950.3060.0960.2790.2450.1330.2060.3200.2670.2140.3140.1751.0000.3060.0560.0300.012
Stage0.0930.1540.0620.1920.1410.0190.1420.2100.1000.1910.5080.1680.1390.2030.0780.3061.0000.0890.0180.000
Tryglicerides0.003-0.1080.2060.1080.2970.3420.322-0.0870.0370.0850.172-0.1810.0150.0070.1640.0560.0891.0000.0910.072
is_male0.1620.0610.0530.0000.1120.0500.1630.0000.0180.0000.0390.0580.0310.0590.0640.0300.0180.0911.0000.039
took_drug0.1200.1120.0500.0000.0820.0780.0750.0000.0000.0000.0590.0680.0730.0900.0900.0120.0000.0720.0391.000

Missing values

2024-01-03T11:26:35.340828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-03T11:26:35.566425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

N_yearsAgeis_maleAscitesHepatomegalySpidersBilirubinCholesterolAlbuminCopperAlk_PhosSGOTTrygliceridesPlateletsProthrombinStagetook_drugEdema_NEdema_SEdema_Y
010.51780854.0383560.00101.2546.03.3765.01636.0151.9090.0430.010.62.01100
16.76164441.0273970.00001.1660.04.2294.01257.0151.90155.0227.010.02.01100
20.13972636.0246580.00102.0151.02.9646.0961.069.75101.0213.013.04.00001
36.38356256.1917810.00000.6293.03.8540.0554.0125.5556.0270.010.62.01100
44.42465860.0109590.00101.4277.02.97121.01110.0125.00126.0221.09.81.01100
53.92602756.1917810.00000.8198.03.9438.0911.057.3556.0280.09.81.01100
64.89041152.2191780.00000.4273.03.6525.0671.084.00177.0284.09.93.00100
75.27397354.7780820.00101.8244.03.2664.06121.860.6392.0183.010.34.01010
80.11232965.9287670.011017.9178.02.10220.0705.0338.00229.062.012.94.01100
94.83561678.4931511.00106.4243.03.35380.0983.0158.10154.097.011.22.01010
N_yearsAgeis_maleAscitesHepatomegalySpidersBilirubinCholesterolAlbuminCopperAlk_PhosSGOTTrygliceridesPlateletsProthrombinStagetook_drugEdema_NEdema_SEdema_Y
52612.95068553.3424661.00102.2572.03.8594.02184.085.25154.0278.011.02.01100
52624.26849344.4273970.00000.7217.03.4652.01031.085.25195.0224.09.83.00100
52636.38904162.5643840.00000.6235.03.7023.0834.075.9556.0165.010.62.00100
52643.38082262.4575340.00104.5328.03.2675.01877.093.00134.0234.011.14.00100
52657.04931542.7726030.00000.8217.03.8540.0685.088.35130.0281.09.83.00100
52667.86301433.6410960.00001.3302.03.4375.01345.0145.0044.0181.010.63.00100
52674.84931567.9534250.00000.5219.04.09121.0663.079.0594.0311.09.73.00100
526810.15616446.5479450.00100.8315.04.0913.01637.0170.5070.0426.010.93.01100
52693.33150732.2547950.00000.7329.03.8052.0678.057.00126.0306.010.21.00100
52706.22465859.1780820.00002.0232.03.4218.01636.0170.5083.0213.013.62.01100